Publications
353 results found
Qin C, Moreno RG, Bowles C, et al., 2016, A semi-supervised large margin algorithm for white matter hyperintensity segmentation, 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Publisher: Springer Verlag, Pages: 104-112
Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation.
Kanavati F, Tong T, Misawa K, et al., 2016, Supervoxel Classification Forests for Estimating Pairwise Image Correspondences, Pattern Recognition, Vol: 63, Pages: 561-569, ISSN: 0031-3203
This article presents a general method for estimating pairwise image correspondences,which is a fundamental problem in image analysis. The method consistsof over-segmenting a pair of images into supervoxels. A forest classifier is thentrained on one of the images, the source, by using supervoxel indices as voxelwiseclass labels. Applying the forest on the other image, the target, yields asupervoxel labelling, which is then regularised using majority voting within theboundaries of the target’s supervoxels. This yields semi-dense correspondencesin a fully automatic, unsupervised, efficient and robust manner. The advantageof our approach is that no prior information or manual annotations arerequired, making it suitable as a general initialisation component for variousmedical imaging tasks that require coarse correspondences, such as atlas/patchbasedsegmentation, registration, and atlas construction. We demonstrate theeffectiveness of our approach in two different applications: a) initialisation oflongitudinal registration on spine CT data of 96 patients, and b) atlas-basedimage segmentation using 150 abdominal CT images. Comparison to state-ofthe-artmethods demonstrate the potential of supervoxel classification forestsfor estimating image correspondences.
Cnossen MC, Polinder S, Lingsma HF, et al., 2016, Variation in structure and process of care in traumatic brain injury: Provider profiles of European Neurotrauma Centers participating in the CENTER-TBI study, PLoS ONE, Vol: 11
© 2016 Cnossen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Introduction: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Methods: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. Results: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. Conclusion: Even among high-volume, specialized neurotrauma centers there is substantial variat
Zheng G, Chu C, Belavy DL, et al., 2016, Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge, MEDICAL IMAGE ANALYSIS, Vol: 35, Pages: 327-344, ISSN: 1361-8415
Maier O, Menze BH, von der Gablentz J, et al., 2016, ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI, Medical Image Analysis, Vol: 35, Pages: 250-269, ISSN: 1361-8423
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).
Paragios N, Ferrante E, Glocker B, et al., 2016, (Hyper)-Graphical Models in Biomedical Image Analysis, Medical Image Analysis, Vol: 33, Pages: 102-106, ISSN: 1361-8423
Computational vision, visual computing and biomedical image analysis have made tremendous progress over the past two decades. This is mostly due the development of efficient learning and inference algorithms which allow better and richer modeling of image and visual understanding tasks. Hyper-graph representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the importance of such representations, discuss their strength and limitations, provide appropriate strategies for their inference and present their application to address a variety of problems in biomedical image analysis.
Rueckert D, Glocker B, Kainz B, 2016, Learning clinically useful information from images: Past, present and future, Medical Image Analysis, Vol: 33, Pages: 13-18, ISSN: 1361-8423
Over the last decade, research in medical imaging has made significantprogress in addressing challenging tasks such as image registration and imagesegmentation. In particular, the use of model-based approaches has been keyin numerous, successful advances in methodology. The advantage of modelbasedapproaches is that they allow the incorporation of prior knowledgeacting as a regularisation that favours plausible solutions over implausibleones. More recently, medical imaging has moved away from hand-crafted, andoften explicitly designed models towards data-driven, implicit models thatare constructed using machine learning techniques. This has led to majorimprovements in all stages of the medical imaging pipeline, from acquisitionand reconstruction to analysis and interpretation. As more and more imagingdata is becoming available, e.g., from large population studies, this trend islikely to continue and accelerate. At the same time new developments inmachine learning, e.g., deep learning, as well as significant improvementsin computing power, e.g., parallelisation on graphics hardware, offer newpotential for data-driven, semantic and intelligent medical imaging. Thisarticle outlines the work of the BioMedIA group in this area and highlightssome of the challenges and opportunities for future work.
Simpson JP, Kane AD, Glocker B, et al., 2016, HYPONATRAEMIC AND HYPO-OSMOTIC STATES ON ADMISSION ARE ASSOCIATED WITH INCREASED CONTUSION AND OEDEMA MEASURED ON MR IMAGING, 12th Symposium of the International-Neurotrauma-Society, Publisher: MARY ANN LIEBERT, INC, Pages: A19-A19, ISSN: 0897-7151
Yao J, Vrtovec T, Zheng G, et al., 2016, Computational Methods and Clinical Applications for Spine Imaging, Publisher: Springer International Publishing, ISBN: 978-3-319-55050-3
Vrtovec T, Yao J, Glocker B, et al., 2016, Computational Methods and Clinical Applications for Spine Imaging, Publisher: Springer International Publishing, ISBN: 978-3-319-41827-8
Alansary A, Lee M, Kainz B, et al., 2015, Automatic Brain Localisation in Foetal MRI using Superpixel Graphs, ICML Workshop on Machine Learning meets Medical Imaging, Publisher: Springer, ISSN: 0302-9743
Guzman-Rivera A, Kohli P, Glocker B, et al., 2015, Tracking using Sensor Data, US2015347846
Glocker B, Konukoglu E, Haynor DR, 2015, Random Forests for Localization of Spinal Anatomy, Medical Image Recognition, Segmentation and Parsing, Editors: Zhou, Publisher: Elsevier, ISBN: 9780128025819
Glocker B, Paragions N, Zabih R, 2015, Note Special Issue on Discrete Graphical Models in Biomedical Image Analysis., Publisher: Elsevier
Richmond D, Kainmueller D, Glocker B, et al., 2015, Uncertainty-driven Forest Predictors for Vertebra Localization and Segmentation, Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer, Pages: 653-660, ISSN: 0302-9743
Accurate localization, identification and segmentation of vertebraeis an important task in medical as well as biological image analysis.The prevailing approach to solve such a task is to first generatepixel-independent features for each vertebra, e.g. via a random forestpredictor, which are then fed into an MRF-based objective to infer theoptimal MAP solution of a constellation model. We abandon this static,two-stage approach and mix feature generation with model-based inferencein a new, more flexible, way. We evaluate our method on two datasets with different objectives. The first is semantic segmentation of a 21-part developing spine of zebrafish in microscopy images, and the secondis localization and identification of vertebrae in benchmark human CT.
Zimmer V, Glocker B, Aljabar P, et al., 2015, Learning and combining image similarities for neonatal brain population studies, International Workshop on Machine Learning in Medical Imaging (MLMI), Publisher: Springer International Publishing, Pages: 110-117, ISSN: 0302-9743
The characterization of neurodevelopment is challenging due to the complex structural changes of the brain in early childhood. To analyze the changes in a population across time and to relate them with clinical information, manifold learning techniques can be applied. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure in the embedding and highly application dependent. It has been shown that the combination of several notions of similarity and features can improve the new representation. However, how to combine and weight different similarites and features is non-trivial. In this work, we propose to learn the neighborhood structure and similarity measure used for manifold learning through Neighborhood Approximation Forests (NAFs). The recently proposed NAFs learn a neighborhood structure in a dataset based on a user-defined distance. A characterization of image similarity using NAFs enables us to construct manifold representations based on a previously defined criterion to improve predictions regarding structural and clinical information. In particular, NAFs can be used naturally to combine the affinities learned from multiple distances in a joint manifold towards a more meaningful representation and an improved characterization of the resulting embedding. We demonstrate the utility of NAFs in manifold learning on a population of preterm and in term neonates for classification regarding structural volume and clinical information.
Kanavti F, Tong T, Misawa K, et al., 2015, Supervoxel classification forests for estimating pairwise image correspondences, International Workshop on Machine Learning in Medical Imaging (MLMI), Publisher: Springer International Publishing, Pages: 94-101, ISSN: 0302-9743
This paper proposes a general method for establishing pairwise correspondences, which is a fundamental problem in image analysis. The method consists of over-segmenting a pair of images into supervoxels. A forest classifier is then trained on one of the images, the source, by using supervoxel indices as voxelwise class labels. Applying the forest on the other image, the target, yields a supervoxel labelling which is then regularized using majority voting within the boundaries of the target’s supervoxels. This yields semi-dense correspondences in a fully automatic, efficient and robust manner. The advantage of our approach is that no prior information or manual annotations are required, making it suitable as a general initialisation component for various medical imaging tasks that require coarse correspondences, such as, atlas/patch-based segmentation, registration, and atlas construction. Our approach is evaluated on a set of 150 abdominal CT images. In this dataset we use manual organ segmentations for quantitative evaluation. In particular, the quality of the correspondences is determined in a label propagation setting. Comparison to other state-of-the-art methods demonstrate the potential of supervoxel classification forests for estimating image correspondences.
Kamnitsas K, Chen L, Ledig C, et al., 2015, Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI, MICCAI Brain Lesion Workshop 2015
Kamnitsas K, Ledig C, Newcombe VFJ, et al., 2015, Segmentation of Traumatic Brain Injuries with Convolutional Neural Networks, 2nd Turku Traumatic Brain Injury Symposium
Whelan T, Leutenegger S, Salas-Moreno RF, et al., 2015, ElasticFusion: Dense SLAM without a Pose Graph, Robotics: Science and Systems, Publisher: Robotics: Science and Systems, ISSN: 2330-765X
Glocker B, Shotton J, Criminisi A, et al., 2015, Real-time RGB-D camera relocalization via randomized ferns for keyframe encoding, IEEE Transactions on Visualization and Computer Graphics, Vol: 21, Pages: 571-583, ISSN: 1077-2626
Recovery from tracking failure is essential in any simultaneous localization and tracking system. In this context, we explore an efficient keyframe-based relocalization method based on frame encoding using randomized ferns. The method enables automatic discovery of keyframes through online harvesting in tracking mode, and fast retrieval of pose candidates in the case when tracking is lost. Frame encoding is achieved by applying simple binary feature tests which are stored in the nodes of an ensemble of randomized ferns. The concatenation of small block codes generated by each fern yields a global compact representation of camera frames. Based on those representations we define the frame dissimilarity as the block-wise hamming distance (BlockHD). Dissimilarities between an incoming query frame and a large set of keyframes can be efficiently evaluated by simply traversing the nodes of the ferns and counting image co-occurrences in corresponding code tables. In tracking mode, those dissimilarities decide whether a frame/pose pair is considered as a novel keyframe. For tracking recovery, poses of the most similar keyframes are retrieved and used for reinitialization of the tracking algorithm. The integration of our relocalization method into a hand-held KinectFusion system allows seamless continuation of mapping even when tracking is frequently lost.
Kostelec PD, Carlin LM, Glocker B, 2015, Learning to Detect and Track Cells for Quantitative Analysis of Time-Lapse Microscopic Image Sequences, IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE
Perera S, Barnes N, He X, et al., 2015, Motion Segmentation of Truncated Signed Distance Function based Volumetric Surfaces, IEEE Winter Conference on Applications of Computer Vision, Publisher: IEEE
Noonan PJ, Ma J, Cole D, et al., 2015, Simultaneous Multiple Kinect v2 for Extended Field of View Motion Tracking, IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Publisher: IEEE, ISSN: 1095-7863
Yao J, Glocker B, Klinder T, et al., 2015, Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, Publisher: Springer International Publishing
Menze BH, Jakab A, Bauer S, et al., 2014, The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), IEEE Transactions on Medical Imaging, Vol: 34, Pages: 1993-2024, ISSN: 1558-254X
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.
Salas-Moreno R, Glocker B, Kelly P, et al., 2014, Dense planar SLAM, International Symposium on Mixed and Augmented Reality (ISMAR), Publisher: Institute of Electrical and Electronics Engineers, Pages: 367-368
Using higher-level entities during mapping has the potential to improve camera localisation performance and give substantial perception capabilities to real-time 3D SLAM systems. We present an efficient new real-time approach which densely maps an environment using bounded planes and surfels extracted from depth images (like those produced by RGB-D sensors or dense multi-view stereo reconstruction). Our method offers the every-pixel descriptive power of the latest dense SLAM approaches, but takes advantage directly of the planarity of many parts of real-world scenes via a data-driven process to directly regularize planar regions and represent their accurate extent efficiently using an occupancy approach with on-line compression. Large areas can be mapped efficiently and with useful semantic planar structure which enables intuitive and useful AR applications such as using any wall or other planar surface in a scene to display a user's content.
Shotton J, Glocker B, Zach C, et al., 2014, Camera/Object Pose from Predicted Coordinates, US2014241617
Marcu L, French PMW, Elson DS, 2014, Preface, Publisher: CRC Press
The text introduces these techniques within the wider context of fluorescence spectroscopy and describes basic principles underlying current instrumentation for fluorescence lifetime imaging and metrology (FLIM).
Zikic D, Glocker B, Criminisi A, 2014, Encoding atlases by randomized classification forests for efficient multi-atlas label propagation, Medical Image Analysis, Vol: 18, Pages: 1262-1273, ISSN: 1361-8423
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